json_to_csvSerialize an array of JSON objects (or arrays) to CSV (keyless, offline). Object keys become the header (union of all keys, in first-seen order). Fields are quoted when needed.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes | An array of objects (or arrays) to serialize. | |
| delimiter | No | Field delimiter (default ","). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "rows": [
+ {
+ "age": 30,
+ "city": "New York",
+ "name": "Alice"
+ },
+ {
+ "age": 25,
+ "city": "Los Angeles",
+ "name": "Bob"
+ }
+ ]
+ },
+ {
+ "delimiter": "\t",
+ "rows": [
+ {
+ "price": 9.99,
+ "product": "Widget",
+ "quantity": 100
+ },
+ {
+ "price": 19.99,
+ "product": "Gadget",
+ "quantity": 50
+ }
+ ]
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint, idempotentHint, and openWorldHint, indicating safe, deterministic behavior. The description adds details about header merging and quoting, but 'offline' is undefined and could mislead agents expecting network dependencies. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with action, no extraneous words. Every sentence adds value: main purpose, then key behavioral details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given rich annotations and full schema coverage, the description adequately covers input-output transformation. However, missing details about return format (CSV string vs file), 'offline' meaning, and potential size limits. Not a major gap but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds semantic context about how 'rows' keys become headers and when quoting occurs, which goes beyond bare schema definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'Serialize' and the resource 'array of JSON objects to CSV', with specific details about header formation (union of all keys in first-seen order) and quoting behavior. It differentiates from sibling 'csv_to_json' which would perform the inverse operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'csv_to_json' or other serialization tools. The phrase 'keyless, offline' is ambiguous and does not clarify use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools overlap in purpose: ask_pipeworx_beta is explicitly identical to ask_pipeworx today, and the polymarket_* family plus bet_research all touch prediction-market analysis. The descriptions are extremely detailed and mostly disambiguate, but an agent must rely on very long text to avoid misselection.
Names are almost all snake_case and readable, but the pattern is mixed: some are verb-first (resolve_entity, list_subscriptions), some noun-first (entity_profile, polymarket_edges), and some are one-word verbs (remember, forget). The ask_pipeworx_* and recent_* prefixes are consistent, but there is no single verb_noun convention throughout.
33 tools is well over the 25+ threshold, especially for a server named 'Csv' that contains only two CSV-specific tools. The rest spans several unrelated domains: data research, prediction markets, subscriptions, memory, AI visibility, and package scanning. The set feels like multiple servers bundled together rather than one well-scoped surface.
As a broad data-research platform, coverage is strong: lookup, grounded answers, deep research, entity resolution, profiles, comparisons, fact-checking, subscription lifecycle, and memory persistence are all present. Minor gaps exist, such as no direct fetch tool for pipeworx:// citation URIs and no subscription-update tool, but most workflows have no dead ends.